Abu-Mostafa, Yaser S. (1988) Connectivity Versus Entropy. In: Neural Information Processing Systems. American Institute of Physics , New York, NY, pp. 1-8. ISBN 0883185695. https://resolver.caltech.edu/CaltechAUTHORS:20160107-155110636
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Abstract
How does the connectivity of a neural network (number of synapses per neuron) relate to the complexity of the problems it can handle (measured by the entropy)? Switching theory would suggest no relation at all, since all Boolean functions can be implemented using a circuit with very low connectivity (e.g., using two-input NAND gates). However, for a network that learns a problem from examples using a local learning rule, we prove that the entropy of the problem becomes a lower bound for the connectivity of the network.
| Item Type: | Book Section | ||||||
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| Additional Information: | © 1988 AIP. | ||||||
| Record Number: | CaltechAUTHORS:20160107-155110636 | ||||||
| Persistent URL: | https://resolver.caltech.edu/CaltechAUTHORS:20160107-155110636 | ||||||
| Usage Policy: | No commercial reproduction, distribution, display or performance rights in this work are provided. | ||||||
| ID Code: | 63467 | ||||||
| Collection: | CaltechAUTHORS | ||||||
| Deposited By: | Kristin Buxton | ||||||
| Deposited On: | 19 Jan 2016 22:59 | ||||||
| Last Modified: | 03 Oct 2019 09:28 |
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